Global Sensor Fusion Market for Automotive (2021 to 2030) - Development of Autonomous Vehicles Presents Opportunities

Dublin, Oct. 13, 2021 (GLOBE NEWSWIRE) -- The "Global Sensor Fusion Market for Automotive by Technology (Camera, LIDAR & RADAR), Data Fusion Type & Level (Homogeneous, Heterogeneous, Data, Decision, Feature), Software Layer, Vehicle Type (ICE, Autonomous & Electric) and Region - Forecast to 2030" report has been added to's offering.

The ICE sensor fusion market for automotive is projected to reach USD 22.2 billion by 2030 from an estimated USD 2.9 billion in 2021, at a CAGR of 25.4% during the forecast period.

Various governments globally are implementing safety standards by making safety features such as automated emergency brake, adaptive cruise control, lane departure warning a mandatory feature in vehicles, driving adoption of cameras, radars and LiDARs in automobiles.

This is expected to strongly drive the adoption of sensor fusion in developed as well developing countries. Also, growing popularity of high-end and luxury cars is boosting sensor fusion market for automotive. Countries such as India, China, Brazil, Mexico, Argentina, European Union, US are focusing on enhanced automotive safety standards. Thus, the demand is expected to gain momentum globally.

Heterogeneous fusion type is expected to be the largest market by data fusion type

The key benefits of heterogenous sensor fusion are enhanced system performance and robustness. Examples of multi-modal fusion systems or heterogeneous sensors are visible cameras, Far IR cameras, visual cameras LASER scanner radar, GPS localizer CAN bus Gyroscope, etc. Various modern sensor networks are heterogenous - a combination of a variety of wired and wireless sensors/actuators.

For instance, in a driver assistance system, the system collects data from internal and external sensors installed in the car. This includes various types of sensors such as GPS localizers, a CAN bus, a gyroscope, radar, and cameras. Thus, the multiple benefits offered by heterogeneous sensor data fusion are driving its popularity in vehicles.

Decision fusion market segment is expected to be the fastest

In decision level sensors, each sensor makes an individual decision before forming a combination of decisions to arrive at a more informed final decision, i.e., target decision fusion. Decision fusion is less complex than data fusion. Decision-making algorithms, as a key technology for uncertain data fusion, is the core to obtain reasonable multisensory information fusion results.

Thus, there is a broad application of decision-making algorithms on target attributes, characteristics, and types through detailed processing of information obtained through various sensors. A multitude of theorems and algorithms are emerging in decision sensor fusions. Decision fusion is expected to gain popularity globally in the coming years, owing to its advantages and less complex architecture.

One of the many practical benefits offered by decision fusion is that it allows combining individual results, even if it was not expected in the testing of the algorithm. Consequently, different sources of information can be easily exchanged, and the fusion strategy is readily adapted to unknown future changes of input sources.

Asia Pacific market is expected to register the highest growth during the forecast period

The Asia Pacific sensor fusion market for automotive is estimated to be the fastest-growing regional market. The growing adoption of advanced ADAS technologies in China, Japan, South Korea, and India is expected to drive market growth in the region. China's passenger car production is expected to reach 24 million units by 2026, presenting a huge opportunity for sensor fusion hardware manufacturers and software/algorithm developers globally as well as domestically.

Not only passenger cars but trucks are also set to reach 2 million units by 2026. The South Korean transport ministry announced that it requires all new large passenger vehicles and trucks to be fitted with AEB and LDW systems from January 2019. Thus, the implementation of government mandates is expected to drive the adoption of sensors- cameras, radars and LiDARs. Such factors would in turn, drive the growth of sensor fusion technology during the forecast period.

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary
3.1 Pre & Post COVID-19 Scenario
3.2 Report Summary

4 Premium Insights
4.1 Attractive Opportunities In Sensor Fusion Market for Automotive
4.2 Sensor Fusion Market for Automotive, by Data Fusion Type
4.3 Sensor Fusion Market for Automotive, by Vehicle Type
4.4 Sensor Fusion Market for Automotive, by Technology
4.5 Sensor Fusion Market for Automotive, by Fusion Level
4.6 Sensor Fusion Market for Autonomous Vehicles, by Level of Autonomy
4.7 Sensor Fusion Market for Electric Vehicles, by Vehicle Type
4.8 Sensor Fusion Market for Automotive, by Region

5 Market Overview
5.1 Introduction
5.2 Market Dynamics
5.2.1 Drivers Technical advantages offered by sensor fusion Stringent emission standards regarding NOx and particulate matter
5.2.2 Restraints Lack of standardization in software architecture/hardware platforms
5.2.3 Opportunities Development of autonomous vehicles
5.2.4 Challenges Security and safety concerns
5.2.5 Impact of COVID-19 On Sensor Fusion Market for Automotive
5.3 Trends/Disruptions Impacting Customer'S Business
5.4 Pricing Analysis
5.5 Value Chain Analysis
5.6 Patent Analysis
5.7 Ecosystem/Market Map
5.8 Sensor Fusion Market for Automotive, Scenarios (2019-2030)
5.8.1 Most Likely Scenario
5.8.2 High COVID-19 Impact Scenario
5.8.3 Low COVID-19 Impact Scenario
5.9 Porter'S Five forces Analysis
5.9.1 Threat of New Entrants
5.9.2 Threat of Substitutes
5.9.3 Bargaining Power of Suppliers
5.9.4 Bargaining Power of Buyers
5.9.5 Intensity of Competitive Rivalry

6 Sensor Fusion Market for Automotive, by Environment
6.1 Introduction
6.2 Internal Sensors
6.3 External Sensors

7 Sensor Fusion for Automotive: Algorithms
7.1 Introduction
7.2 Kalman Filter
7.3 Bayesian Filter
7.4 Central Limit theorem
7.5 Convolutional Neural Networks

8 Sensor Fusion Market for Automotive, by Technology
8.1 Introduction
8.1.1 Research Methodology
8.1.2 Assumptions/Limitations
8.1.3 Industry Insights
8.2 Cameras
8.2.1 Technical Advantages Such As Reading Signs & Classifying Objects Boost Demand for Cameras
8.3 Radar
8.3.1 Affordability and Clarity In Challenging Conditions Expected To Drive Radar Demand
8.4 LiDAR
8.4.1 Enhanced Obstacle Detection & Safe Navigation Boost Application In Vehicles

9 Sensor Fusion Market for Automotive, by Fusion Level
9.1 Introduction
9.1.1 Research Methodology
9.1.2 Assumptions/Limitations
9.1.3 Industry Insights
9.2 Feature Fusion
9.2.1 Accuracy of Feature Level Fusion Drives Its Popularity
9.3 Decision Fusion
9.3.1 Developments In Algorithms for Decision Fusion Boost Growth
9.4 Data Fusion
9.4.1 Lower Detection Error Probability Drives Segment Growth

10 Sensor Fusion Market for Automotive, by Vehicle Type
10.1 Introduction
10.1.1 Research Methodology
10.1.2 Assumptions/Limitations
10.1.3 Industry Insights
10.2 Passenger Cars
10.2.1 Implementation of Regulations To Make ADAS Standard In Passengers Cars Drives Segment
10.3 Light Commercial Vehicles (LCV)
10.3.1 Safety Regulations To Reduce Accidents Boosts Adoption of Sensor Fusion In Lcvs
10.4 Heavy Commercial Vehicles (HCV)
10.4.1 Segment Driven by Adoption of ADAS Features In Hcvs

11 Sensor Fusion Market for Automotive, by Data Fusion Type
11.1 Introduction
11.1.1 Research Methodology
11.1.2 Assumptions/Limitations
11.1.3 Industry Insights
11.2 Homogenous
11.2.1 Homogenous Fusion To Witness Moderate Growth During forecast Period
11.3 Heterogenous
11.3.1 Growing Demand for Premium Vehicles With Sensor Fusion Expected To Drive Demand

12 Sensor Fusion Market for Automotive, by Software Layer
12.1 Introduction
12.1.1 Research Methodology
12.1.2 Assumptions/Limitations
12.1.3 Industry Insights
12.2 Operating System
12.2.1 Ongoing Developments In Advanced Software Operating Systems Drive Popularity
12.3 Middleware
12.3.1 Availability of Various Middleware Expected To Boost Market
12.4 Application Software
12.4.1 Developments In Application Software With More Advanced Features Expected To Drive Adoption

13 Sensor Fusion Market for Electric Vehicles, by Vehicle Type
13.1 Introduction
13.1.1 Research Methodology
13.1.2 Assumptions/Limitations
13.1.3 Industry Insights
13.2 Battery Electric Vehicles (Bev)
13.2.1 Regulations To Mandate ADAS Features In Bevs Boost Segment
13.3 Plug-In Hybrid Electric Vehicles (PHEV)
13.3.1 Increasing Sales of PHEVs With ADAS Features Boost Segment
13.4 Fuel-Cell Electric Vehicles (FCEV)
13.4.1 Launch of FCEV Models With ADAS Features To Drive Growth

14 Sensor Fusion Market for Autonomous Vehicles, by Level of Autonomy
14.1 Introduction
14.1.1 Research Methodology
14.1.2 Assumptions/Limitations
14.1.3 Industry Insights
14.2 L4
14.2.1 Segment Propelled by Oem Investment In Automated Driving
14.3 L5
14.3.1 Increased Testing of Autonomous Driving Boosts Advancements In L5

15 Sensor Fusion Market for Automotive, by Region

16 Automotive Sensors Market, by Sensor Type
16.1 Introduction
16.2 Temperature Sensors
16.2.1 Temperature Sensors Mainly Used In Powertrain and HVAC Applications
16.3 Pressure Sensors
16.3.1 Pressure Sensors Mainly Used In HVAC, Safety & Control, and TPMS
16.4 Position Sensors
16.4.1 Position Sensors Widely Used To Provide Information To ECMS
16.5 Oxygen Sensors
16.5.1 Oxygen Sensors Used To Measure Proportional Amount of Oxygen In Liquid Or Gas
16.6 Nitrogen Oxide Sensors
16.6.1 Stringent Government Regulations To Limit NOx Emissions To Provide Opportunities for NOx Sensors
16.7 Speed Sensors
16.7.1 Speed Sensors Used To Measure Engine Camshaft Speed and Vehicle Speed
16.8 Inertial Sensors
16.8.1 Inertial Sensors Mainly Based On Mems Technology and Used In Accelerometers and Gyroscopes Accelerometers Gyroscopes
16.9 Image Sensors
16.9.1 Increasing Adoption of ADAS To Boost Use of Image Sensors CMOS CCD
16.10 Other Sensors
16.10.1 Radar
16.10.2 Ultrasonic Sensors
16.10.3 Rain Sensors
16.10.4 Relative Humidity Sensors
16.10.5 Proximity Sensors
16.10.6 Particulate Matter Sensors
16.10.7 LiDAR
16.10.8 Current Sensors

17 Automotive Sensors Market, by Application

18 Recommendations by the Publisher
18.1 Asia Pacific: A Potential Market for Sensor Fusion Market for Automotive
18.2 Strategic Adoption of LiDAR To Create New Revenue Pockets
18.3 Growing Demand for Sensor Fusion In Electric & Autonomous Vehicles
18.4 Conclusion

19 Competitive Landscape
19.1 Overview
19.2 Key Player Strategies/Right To Win
19.3 Revenue Analysis of Top Five Players, 2018-2020
19.4 Market Share Analysis
19.5 Competitive Leadership Mapping
19.5.1 Star
19.5.2 Emerging Leader
19.5.3 Pervasive
19.5.4 Participant
19.6 Competitive Scenario
19.7 New Product Launches
19.8 Agreements, Partnerships, Collaborations, and Joint Ventures

20 Company Profiles
20.1 Key Players
20.1.1 Robert Bosch GmbH
20.1.2 Continental AG
20.1.3 NXP Semiconductors N.V.
20.1.4 STMicroelectronics
20.1.5 ZF Friedrichshafen AG
20.1.6 Infineon Technologies
20.1.7 Allegro Microsystems
20.1.8 Denso Corporation
20.1.9 Sensata Technologies, Inc.
20.1.10 Elmos Semiconductor Se
20.1.11 TE Connectivity Ltd.
20.2 Other Key Players
20.2.1 CTS Corporation
20.2.2 Baselabs GmbH
20.2.3 Memsic Semiconductor (Tianjin) Co., Ltd.
20.2.4 Kionix, Inc.
20.2.5 TDK Corporation
20.2.6 Analog Devices
20.2.7 Microchip Technology Inc.
20.2.8 Monolithic Power Systems, Inc.
20.2.9 Leddartech Inc.
20.2.10 Ibeo Automotive Systems GmbH
20.2.11 Maxim Integrated
20.2.12 Velodyne LiDAR, Inc.
20.2.13 Renesas Electronics Corporation
20.2.14 Mobileye
20.2.15 Aptiv Plc
20.2.16 Magna International

21 Appendix

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